Processing, May 2018

Cover Series Preventive Maintenance Self service predictive analytics A n i n d u s t r i a l f o r t u n e t e l l e r This approach allows subject matter experts to take proactive measures to reduce downtime and avoid unnecessary risks By Artur Beyer TrendMiner hanging raw material prices commoditization increasing regulatory stringency and various other market dynamics all impact process industry margins In such an active C market it is necessary to be as exible as possible without tying up big amounts of capital over years to come is article looks at how a golden ngerprint can be created by modeling past successes is means that brown eld plants can instead look toward a sustainable future by utilizing process expertise in combination with the plethora of stored process data One area of improvement is predictive maintenance in order to decrease unplanned downtime Recently a lot has been written about predictive operational performance within process manufacturing industries The bulk of articles is related to predictive maintenance where applied sensors are used to analyze and predict equipment behavior is is mainly focused on the most critical assets through centrally led time consuming and expensive predictive data modeling projects To get operational performance prediction for all assets a new analytics approach is required self service predictive analytics for the subject matter experts A smarter approach Predictive maintenance solutions have traditionally involved data scientists or central improvement experts for building comprehensive analytics models Aside from being costly and time consuming this way of working has other major disadvantages it creates a bottleneck in the organization underutilizes subject matter experts and leaves many smaller predictive analytics cases unaddressed A new integrated and more e cient approach is self service analytics is method does not require the expertise of a data scientist nor does it require an extensive overhaul to ones existing infrastructure The insights into process and asset behaviors are based on a wealth of historical and real time data and with this information subject matter experts can take proactive measures to reduce downtime and avoid unnecessary risks Different ways to execute a powerful strategy ere are three di erent ways to use this approach Event based If a certain signature behavior is detected which can affect another part in the process that typically occurs later a noti cation can be generated Probabilistic e current behavior is interpreted and a likeliness of future behavior is calculated Regressive The prediction is based on certain conditions that must be met and veri ed An example of how self service analytics can be practical in use is by creating ngerprints which are created using any variety of signature patterns found in the past By using de ned ngerprints for event based 18 Processing MAY 2018 An example of how self service analytics can be practical in use is by creating ngerprints which are created using any variety of signature patterns found in the past Figure 1 Three ways of self service predictive analytics All images courtesy of TrendMiner olm26250 iStock

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